How would you approach analyzing a dataset that contains missing or inconsistent data?

During my final year in college, I worked on a project where we analyzed survey data from students regarding their study habits. We found that several responses were missing or inconsistent. To address this, I first categorized the missing data and determined if it was random or systematic. For the random missing data, I applied mean imputation to fill in gaps, while for systematic missing data, I conducted follow-up surveys to clarify uncertainties. This not only improved the dataset's quality but also taught me the importance of thorough data validation before analysis. Ultimately, our findings helped the college understand better study patterns, which led to the implementation of new study resources.

While volunteering for a community health initiative, I was tasked with analyzing health records to identify trends in service utilization. I noticed that many records had missing entries, particularly in patient demographics. To handle this, I organized a workshop to educate volunteers on accurate data entry practices. Additionally, I matched existing records with outreach reports to fill in gaps. This collaborative approach not only resolved inconsistencies but also enhanced our understanding of community needs. By sharing the cleaned dataset with local health authorities, we contributed to better healthcare planning.

In my first job as a retail analyst, I frequently dealt with sales data that had missing entries due to various reasons, such as system errors. My approach involved conducting a preliminary analysis to understand the extent of the missing data. I then used a combination of forward filling for continuous data and mode imputation for categorical data to ensure we had a complete dataset. This method allowed us to maintain the integrity of our reports, leading to actionable insights that improved inventory management. It was a valuable experience that reinforced the importance of addressing data quality issues.